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Record W4413435870 · doi:10.1017/mdh.2025.10026

Little lives—reading between the lines: insights from the Northampton Infirmary Eighteenth Century Child Admission Database

2025· article· en· W4413435870 on OpenAlexafffund
Madeleine Mant, Judy Chau, Bryce Hull, Maryam Khan, Mollie Sheptenko, Mia Taranissi, Chris Parry, Fred O’Dell, A N Williams

Bibliographic record

VenueMedical History · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies on Reproduction, Gender, Health, and Societal Changes
Canadian institutionsThe Scarborough HospitalRoche (Canada)University of Toronto
FundersJackman Humanities Institute, University of TorontoUniversity of Cambridge
KeywordsReading (process)World Wide WebComputer scienceData scienceMedicineLibrary scienceDatabasePolitical science

Abstract

fetched live from OpenAlex

The presence of children in eighteenth-century English voluntary hospitals is an area of increasing interest and attention. The Northampton Infirmary admission records detail inpatient and outpatient ages from 1744 to 1804, allowing for longitudinal investigations of children in the institution. The most common distempers affecting children were surgical infections, infectious diseases, and skin diseases; fifty-six per cent of the child patients were male and 43.3 per cent were female. Nearly seventy-five per cent of children left the hospital 'cured'. This article outlines the Northampton Infirmary Eighteenth Century Child Admission Database, and demonstrates how the patterning of distempers within and among children provides insight into the health journeys of eighteenth-century children through the lens of their bodies, their parents, and their institutional recommenders.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.299

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.011
Science and technology studies0.0040.002
Scholarly communication0.0050.006
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.041
GPT teacher head0.255
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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